- The paper introduces a zero-shot guided diffusion framework that produces dense depth maps from sparse inputs without additional fine-tuning.
- It leverages a pretrained latent diffusion model with test-time optimization to seamlessly align sparse depth cues with metric scales.
- Marigold-DC demonstrates strong generalization across indoor and outdoor environments, enabling robust depth completion in real-world scenarios.
An Overview of Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
The paper "Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion" presents a pioneering approach for improving depth completion in computer vision, especially when faced with sparse depth data. Depth completion, a process of generating dense depth maps from sparse depth inputs guided by an RGB (or grayscale) image, is a critical task in various applications like robotics, autonomous navigation, and 3D modeling. Existing methods often face challenges when encountering data beyond their training distribution or in instances of extreme sparsity. Marigold-DC addresses these challenges innovatively by leveraging state-of-the-art monocular depth estimation techniques integrated with a guided diffusion process.
Central to this work is the reframing of depth completion as an image-conditional depth generation task, using a pretrained latent diffusion model designed for monocular depth estimation. Marigold-DC introduces sparse depth observations as guidance by applying a test-time optimization scheme that is interlaced within the iterative inference process of the denoising diffusion model. Importantly, Marigold-DC does not necessitate any fine-tuning of the original Marigold model, thereby preserving both the model's innate prior knowledge and minimizing additional training effort.
The method hinges on the progress made by modern monodepth estimators, which benefit from foundational models capable of handling diverse and complex scenes. Here, a pretrained latent diffusion model (LDM) for monodepth estimation acts as the prior. Marigold-DC optimizes this model at test time, dynamically aligning sparse depth cues with metric scale and shift adjustments. Such an approach takes advantage of the diffusion models' iterative nature, utilizing Denoising Diffusion Probabilistic Models (DDPMs) to guide output according to specific test-time signals.
Marigold-DC claims significant zero-shot generalization capabilities across diverse real-world datasets, spanning indoor and outdoor scenes. This is a notable advancement, given that conventional depth completion models often falter when transferred to unseen domains. Noteworthy is Marigold-DC's capacity to achieve high-quality depth predictions from even very sparse data, which poses a challenge for methods confined by their training scenarios.
In terms of practical and theoretical implications, Marigold-DC opens new avenues for depth completion methodologies, inherently suggesting that alignment with monocular depth priors could be more beneficial than solely relying on in-domain learned structures when addressing data sparsity and domain shifts. It showcases the potential for leveraging sophisticated diffusion-based models to act as strong priors in visual tasks that necessitate generalization.
Marigold-DC signifies a transition towards utilizing advanced generative models in practical applications, easing the dependence on domain-specific training and opening discussions around leveraging pre-trained diffusion models in broader vision tasks. Future research could explore optimizing inference speeds and broadening the range of contexts and sensor configurations that such guided diffusion approaches can tackle.
This work importantly highlights the intersection of generative AI models and practical, application-centered vision tasks, positing that deeper integrations of these technologies hold promise for robust, adaptable vision systems capable of interfacing seamlessly across varied environments and sensor inputs.